Determination of CYP2C19 Polymorphism, Side Effects, and Medication Adherence in Patients Who have Utilized Selective Serotonin Reuptake Inhibitors
Bibliographic record
Abstract
Objective: The aim of this study is to determine relationship of cytochrome P-450 2C19 (CYP2C19) enzymes polymorphism, side effects, and medication adherence in patients who have been diagnosed with major depression and have utilized selective serotonin reuptake inhibitors.Methods: Fifty-three major depression patients (mean of age: 33.25±11.29 years old; male/female: 7/46) were included in this study. Polymorphisms were determined from genomic DNA by using the ‘Real-Time Polymerase Chain Reaction’ method. Side effects and medication adherence levels were assessed by using the ‘Toronto Side Effects Scale’ and the four items medication adherence scale (Morisky, Green and Levine), respectively.Results: The most common side effects that patients reported were drowsiness/daytime somnolence (54.7%), malaise or fatigue (43.4%), sweating (43.4%), nausea (41.5%) and dry mouth (41.5%). Only nine (17%) patients were found to be highly adherent to their medication. When evaluating the CYP2C19 polymorphisms of patients, 37.7%, 24.5% and 20.8% of the patients were classified as intermediate, extensive and ultra-rapid metabolizers, respectively. Allele frequencies of CYP2C19*17 and CYP2C19*2 was calculated as 24.5% and 27.4%, respectively. Although there were some differences in side effect scores and medication adherences among the polymorphism groups, these relationships were not found to be statistically significant.Conclusion: This study shows that patients who utilized antidepressants frequently experienced side effects and had low medication adherence. Another interesting finding is the high rate of ultrarapid metabolizers of CYP2C19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".